Systems and methods for automated analysis of business intelligence

ABSTRACT

An embodiment of the present invention is directed to a user friendly dynamic tool for business intelligence automated analytics that enable end users, e.g., employees, etc. to maximize efficiency and flexibility while interacting with data systems across applications. An embodiment of the present invention provides real-time analytics based on Artificial Intelligence (AI) and an interactive Chatbot interface. The innovative framework may be applicable across various lines of business and scenarios.

FIELD OF THE INVENTION

The present invention generally relates to systems and methods forautomated analysis of business intelligence.

BACKGROUND OF THE INVENTION

Often, end users of business intelligence/reporting tools, or similarweb interfaces, need to wait a considerable amount of time, ranging fromhours to days, to get an ad-hoc report that may not be available througha user interface. For reports that are available, user friendly featuresare limited and often require extensive Human Computer Interaction,which means a user is often required to click on several tabs/buttons,or navigate through different link and sources like Help Desk or SubjectMatter Expert to retrieve the required information or customizedreports. For example, when a user is looking for specific information,such as “How many transactions of less than $20 did I make in the last 6months?” or “How many user stories are completed in my project?”, theuser approaches the help desk who in turn approaches production supportteam, or development team that will frame the relevant query and executethe query against one or more databases.

This flow has security risks with the script executioner able to see thedata, which may be sensitive. In addition, there is no learning involvedin the current process with the knowledge confined to the developer orproduction support personnel which may lead to potential single point offailure. Further, the solution gathered is not real time data due to thetime and effort involved. Additionally, the queries are often repetitiveand not dynamic and have little, if any, possibility for reuse.

SUMMARY OF THE INVENTION

According to one embodiment, the invention relates to a system forautomated analysis of business intelligence. The system comprises: aquery interface configured to receive a natural language user input anatural language understanding processor comprising a parser andinterpreter to determine a user intent and generate an intermediatequery and further based on a context manager; a query processorconfigured to translate the intermediate query to a database query andexecute the database query against a database; and a presentationinterface configured to generate a result output that comprises resultsof the database query in natural language.

According to another embodiment, the invention relates to a method forautomated analysis of business intelligence. The method comprises thesteps of: receiving, via a query interface, a natural language userinput; determining, via a natural language understanding processorcomprising a parser and interpreter, a user intent; generating anintermediate query and further based on a context data from a contextmanager; translating, via a query processor, the intermediate query to adatabase query and executing the database query against a database; andgenerating, via a presentation interface, a result output that comprisesresults of the database query in natural language.

The system may include a specially programmed computer system comprisingone or more computer processors, interactive interfaces, electronicstorage devices, and networks.

The computer implemented system, method and medium described hereinprovide unique advantages to entities, organizations and other users,according to various embodiments of the invention. An embodiment of thepresent invention provides real-time analytics based on ArtificialIntelligence (AI) and an interactive Chatbot feature. The innovativeframework is directed to minimizing Human-Computer Interaction (HCI) toprevent and/or minimize user dissatisfaction by improving relevancy ofreports and minimizes effort and time involved to generate the reportsby utilizing Analytics tool. Further, the inventive tool reducescognitive load associated with complex HCI. Additionally, significantbenefits such as, tightened data security, elimination of single pointof failure, access to relevant and real time analytics, customizablereport formats (e.g., PPT, XLS, Word, etc.), formatting features,ability to bookmark frequently used reports may be available incomparatively lesser time and effort than a traditional businessintelligence (BI) tool.

These and other advantages will be described more fully in the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to facilitate a fuller understanding of the present invention,reference is now made to the attached drawings. The drawings should notbe construed as limiting the present invention, but are intended only toillustrate different aspects and embodiments of the invention.

FIG. 1 depicts a system for automated analysis of business intelligence,according to an embodiment of the present invention.

FIG. 2 depicts a method for automated analysis of business intelligence,according to an embodiment of the present invention.

FIG. 3 is an exemplary user interface, according to an embodiment of thepresent invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following description is intended to convey an understanding of thepresent invention by providing specific embodiments and details. It isunderstood, however, that the present invention is not limited to thesespecific embodiments and details, which are exemplary only. It isfurther understood that one possessing ordinary skill in the art, inlight of known systems and methods, would appreciate the use of theinvention for its intended purposes and benefits in any number ofalternative embodiments, depending upon specific design and other needs.

An embodiment of the present invention is directed to a user friendlydynamic tool for business intelligence automated analytics that enableend users, e.g., employees, etc. to maximize efficiency and flexibilitywhile interacting with data systems across applications. An embodimentof the present invention provides real-time analytics based onArtificial Intelligence (AI) and an interactive Chatbot interface. Theinnovative framework may be applicable across various lines of businessand scenarios.

The framework capabilities, including ML and AI, lead to a continuouslearning process where the tool may be self-trained based on user searchpattern and queries. Accordingly, the tool may accurately determine auser's intent with partial queries and further provide the user withchoices for any further relevant information.

Embodiments may incorporate a conversational AI program (e.g., Chatbot)using Natural Language Interface to Database (“NLIDB”) to returnreal-time information to end users. Embodiments may promotecustomer-focused instant analytics, accurately determine an end user'sneeds from a business intelligence perspective, reduce operationalcosts, effectively use machine learning, and provide different optionsto export the one or more results.

Embodiments may solve context-specific questions based on a priorquestion, historical information and/or analysis and may be intuitive torespond to partial queries from an end user. Using machine learning,embodiments may determine a specific intent or need of the user questionand provide an optimal and proactive response. In addition, anembodiment of the present invention may respond to partial queries thatmay be based on prior questions and/or prior interactions.

Embodiments may translate a natural language query provided through aninterface (e.g., a Chatbot interface, user input, etc.) to a databasequery (e.g., Structured Query Language (SQL), other database querylanguage, etc.) that is able to understand various statisticaloperations, including but not limited to “where, when, what, who” typeof questions referenced in the natural language query, while translatingthe input to an intermediate query. The intermediate query may then beconverted to a specific database query to be executed against thedatabase. The result set from the executed query may be translated backto an intermediate language and then to a natural language response or agraphical representation depending on the context. Other outputs and/orformats may be supported.

Embodiments may use an initial or one-time training imparted throughmachine learning algorithms to teach the concepts and the intents. Theconcepts and/or intents may be specific to a database language. This mayinclude various queries that may be supported and/or other specifics.For example, the training may be specific to the database querylanguage. Accordingly, different database query languages may havedifferent training algorithms that may be applied. Alternativeinterpretations may be provided in the query interface to assist theuser in choosing a more meaningful question. Embodiments assume that theuser's query is dynamic by nature, and may start with an exploratoryquery. A context manager may sequentially track recent queries by theuser, or by a user with a similar intent, and may use probabilisticmethodologies to identify the intent behind the query.

Embodiments are scalable and may be introduced in any reporting toolwith plugins to connect to respective databases. The reporting toolwhere the plugin is targeted may be configured to provide the corpus ina predefined format. For example, metadata related to a table maydeclare whether a particular column type is PERSON, LOCATION, CURRENCY,TIME, or other domain specific entities, assuming an exhaustive list ofsuch data definitions is exposed.

According to an embodiment of the present invention, an intermediatequery tree may be tagged to a security context in order to assesswhether a query intent has the necessary entitlements, access and/orrights granted to the logged in user profile.

According to an embodiment of the present invention, an interface may beprovided to certain users to teach new concepts or intents based on thelearned concepts.

Embodiments may subscribe to specific queries, with the interface (e.g.,the Chatbot) providing specific information at the time of access,and/or tagging an interesting insight against another profile or publicprofile for further consumption.

FIG. 1 depicts a system for automated analysis of business intelligence,according to an embodiment of the present invention. System 100 mayinclude query interface 110 that receives a query from a user; naturallanguage (NL) understanding component 120, query processor 130,presentation component 140, context manager 142, and machine language(ML) driven domain dependent knowledge 150.

In one embodiment, query interface 110 may be hosted or executed on anelectronic device, e.g., desktop computer, workstation, tabletcomputers, smart phones, smart watches, Internet of Things (IoT)appliances, web-accessible devices, etc.

Natural language understanding component 120 may include NL parser 122,which may receive the query from query interface 110 and parse the queryinto a NL parser tree or other structure, which may includeparts-of-speech tag sets that may be further mapped to statistical queryvariables and/or corpus synonyms. NL parser 122 may communicate with NLinterpreter 124, which may interpret the parsed query, and to failureanalysis system 126 that receives the NL interpretation and identifiesany failures in the parsing and/or interpretation for further humanintervened analysis and refinement of the model.

Query processor 130 may receive the interpretation from NL interpreter124, and then translate the interpretation into an intermediatelanguage. Search query formulator 134 may generate a search from theintermediate language, and may provide the formulated query to searchengine 136. Natural language parser 122 may be responsible fortokenizing the sentence, lemmatization, removing stop words relevant tothe domain while retaining statistical keywords and corpus relatedentities create parts-of-speech tagger tree, and propagating the parsedobject to NL interpreter 124. NL interpreter 124 may be contextagonistic and may not have the historic knowledge of the conversationflow.

In one embodiment, NL Interpreter 124 may use trained Hidden MarkovModels (“HMM”) while including serialized context from Context Mapper142 and consulting Concept Mapper (154) to predict the right intent. Inone embodiment, NL interpreter 124 may have multiple ranked intents withthe top ranked intent being propagated to intermediate languagetranslator 132. Other models may be implemented.

ML driven domain dependent knowledge 150 may include domain conceptsontology 152, concept mapper 154, acronym dictionary 156, MLentities/word vectors/training 158, and application database (DB) 160.

Domain Concepts Ontology 152 may represent a repository of labelledentities' relevant statistical definitions, such as minimum, maximum,average, percentage, and domain specific entities, and may map databasetable and column definitions to a pre-defined category. For example, therepository may include metadata related to each of the tables,elaborating on how data column and data table should be mapped todifferent entities for the domain.

As a non-limiting illustration, Column A in Table X may be mapped toPERSON, while Column B may be mapped to a LOCATION. Acronym dictionary156 may keep track of the synonyms related to entities for the domainwhere the system is hosted.

The architecture may be generic and may cater to different businessdomains varying from mortgage to workforce management.

Search engine 136 may interface with application DB 160. For example,application DB 160 may store and manage data relevant to the businessdomain where the system would be hosted, serialized context, historicconversational data and any feedback received by the user explicitly orimplicit.

Presentation component 140 may include translators responsible forconverting the result set after executing the NLIDB query into asentence with grammatically correct semantics. Presentation component140 may represent an interactive user interface. For example,Presentation component 140 may render a graph, chart, textual responseand/or other output back to the channel from where the query wasgenerated.

Context manager 142 may be responsible for tracking a history of aconversation through, for example, chatbot memory spanned acrossmultiple slots. For example, values stored in context manager 142 may bepassed to NL interpreter 124. Similarly, while sending the result setback to presentation component 140, context manager 142 may keep trackof an estimated accuracy for and confidence in what is being sent in theresponse. Feedback from query interface 110 may pass through contextmanager 142, which may then modify the ranking of possible responses.

In addition, Context manager 142 may tract user actions and interactionsfrom query interface 110, which may include background requests. Contextmanager 142 may determine user preferences (e.g., user likes ordislikes) including navigational history of the responses, etc. Forexample, a user swiftly scrolling through some response graphs mayindicate the user is ignoring the response while a longer time mayindicate the user is interested in the details. In this example,feedback may be redirected through context manager 142. In response tothe user's interaction (e.g., type of scrolling, etc.), presentationcomponent 140 may generate a corresponding output, such as dynamiccharts based on the ranked responses instead of providing all possibleresponses at one time.

While a single component is shown in FIG. 1, each component mayrepresent a plurality of components. FIG. 1 is a representation exampleof an architecture. Other variations may be implemented. The system ofFIG. 1 may be implemented in a variety of ways. Architecture withinsystem may be implemented as hardware components (e.g., module) withinone or more network elements. It should also be appreciated thatarchitecture within system may be implemented in computer executablesoftware (e.g., on a tangible, non-transitory computer-readable medium)located within one or more network elements. Module functionality ofarchitecture within system may be located on a single device ordistributed across a plurality of devices including one or morecentralized servers and one or more mobile units or end user devices.The architecture depicted in system is meant to be exemplary andnon-limiting. For example, while connections and relationships betweenthe elements of system are depicted, it should be appreciated that otherconnections and relationships are possible. The system described belowmay be used to implement the various methods herein, by way of example.Various elements of the system may be referenced in explaining theexemplary methods described herein.

FIG. 2 depicts a method for automated analysis of business intelligence,according to an embodiment of the present invention. At step 205, a userquery in natural language may be received. At step 210, an embodiment ofthe present invention may determine user intent using NL parsers andcontext information. At step 215, the user intent may be used todetermine an intermediate query through graphs and/or pre-trained MLmodels. At step 220, graph nodes and relationships may be parsed into adatabase query. At step 225, the database query may be executed againsta database. At step 230, results output may be generated and displayedto the user. At step 235, user actions may be tracked for improvedaccuracy through machine learning. The order illustrated in FIG. 2 ismerely exemplary. While the process of FIG. 2 illustrates certain stepsperformed in a particular order, it should be understood that theembodiments of the present invention may be practiced by adding one ormore steps to the processes, omitting steps within the processes and/oraltering the order in which one or more steps are performed.

An embodiment of the present invention is directed to supportingquerying various data sources through a natural language input.

At step 205, a user may enter a query in natural language into a queryinterface (e.g., an application, program, browser, etc.) executed on anelectronic device. Examples may include: “How many transactions did Imake less than $20 in last 6 months?”; “How many MDs are located inBrooklyn?”; “How many user stories are completed in my project ABC?”;“What is the cash to credit spend ration on travel expenses?” The userinput may be in text format, voice format and/or other type ofcommunication. The input may be received and/or inferred from a separateinteraction or application. For example, an input sentence or phrase maybe tokenized to identify various tokens. A context manager may identifyand/or enrich the input with additional context.

At step 210, the system may determine the user's intent of the query.For example, the system may extract the intent of the query using NLparsers. A machine learning model may include a number of intents withtraining examples attached thereto. As a part of a conversation with thechatbot, the user may be engaged in a dialog flow. Each time a newquestion is asked, the Chatbot may determine intent based on aprobabilistic model, whether the new query is related to one of theprevious queries that have been saved in chatbot's memory based on asimilarity ratio.

For example, a parser library may be used to perform parsing.Grammatical relations may be extracted using a pre-trained machinelearning model. For example, nouns and prepositions may be extractedfrom a Parts of Speech Tagger and Typed Dependencies. An embodiment ofthe present invention may apply semantic analysis with nouns andprepositions. A graph tree may be constructed with the nouns as nodeswith prepositions and typed dependencies as edges of graph.

The system may perform various permutations and combinations bysubstituting features in a parsed tree with features that are stored inits memory slots. A substituted feature that gives a betterrepresentation of intent may be selected as a “winner” and thenforwarded to the next component in a pipeline. A substituted featurethat leads to a lower probabilistic model may be kept in a memory graphand assigned a lower rank. The feature may also be discarded when theprobability assigned reaches a lower threshold, for example.

At step 215, the intent may be translated into an intermediate queryusing graphs and/or pre-trained ML models. For example, the trainingmodel may include at least some of the following components: intents,which may be further categorized as “what-when-where, who, which”questions; statistical determiners (e.g., maximum, minimum, average,median, mode, count, percentage, ratio, etc.), a series of examples todistinguish intents, synonyms, etc. The training model may be trained tounderstand the depth and/or level of the natural language query based ondifferent training examples.

At step 220, the graph nodes and relationships may be parsed into adatabase query using, for example, adapters (e.g., Oracle, SQL, etc.).For example, a rules engine may convert the intermediate query into adatabase query. In one embodiment, a SQL syntax may be governed by rulesspecific to the language (e.g., Oracle, MSSQL, etc.). An intermediatelanguage parser may generate a database query being mapped from theadapter itself. While registering a parser with the component, detailsrelevant to the syntax of a SQL query may be provided.

For example, a graph may be converted to a SQL query using a DatabaseAdapter. The graph may also be used generate alternative models behindor beyond the question.

At step 225, the database query may be executed against a databaseand/or other data sources. For a particular database, a specific set ofqueries and/or statements may be permitted. In an example involving SQL,only GET or SELECT queries may be permitted for an analytical query. Inthis example, UPDATE, DELETE, and INSERT may not be permitted. Othervariations may be applied and other scenarios may be implemented.

At step 230, the results of the query may be generated. For example, theresults may be converted to graph and ultimately a natural languageresponse, e.g., text, plot, etc. The response may be provided as atextual response, as a plot, chart or graph, or in any other suitableformat as is necessary and/or desired.

For example, conversion of the graph to a NL Query may ensure that theuser receives the data in a natural language format rather than a singleword response. This may be particularly important for questions, whereusers may expect a binary response, but the chatbot may provideadditional details, such as explaining the reasoning behind the binaryresponse. Response templates may be mapped to question intent with theplaceholders being populated by the component.

At step 235, the user actions in response to the response may be trackedand used for “feed forward” learning. For example, the user's actionsmay be applied to future queries by the user to further refine thequery. During a user interaction, the chatbot may collect informationrelevant to the kind of transition that is occurring during theconversations with respect to nature of the question asked, how muchtime the user is spending reviewing and selecting a response, anyfeedback provided by the user, explaining which of the ranked responseappear more relevant, the amount of time spent in each of the responsescreens, what users with similar traits are showing interest in, etc.

For example, the result set may be transformed into various formats,including grid, chart and/or textual view. The Graph representation ofthe intermediate query in a simplified format, the actual questionasked, and the response may be sent back to the user interface. Theamount of time spent by the user in each grid and the feedback buttonclicked, e.g., Bookmark, Incorrect, Correct or More options, may then bestored for further training and reinforced learning.

In a probabilistic model, a NL interpreter may rank the variousscenarios based on feedbacks received on the above parameters.

For example, a user may input a first question including “How many SWEng work from DE?” A second question may be “From Jersey City?” In thisexample, an embodiment of the present invention may determine entitiesas “worker, location and jobfamily.”

FIG. 3 is an exemplary user interface, according to an embodiment of thepresent invention. As shown in FIG. 3, SQL queries may be executedagainst a database. At input 310, a user may enter a query in naturallanguage. The input may be entered by the user via text or voice,selected from a set of options and/or other input or command. Additionalspecifics may be provided at 312, 314 and 316. These inputs may bepredetermined and available via drop down windows. Other inputs may beprovided. The result set may be transformed into various formats,including text 318, grid 320 and chart 322. The Graph representation ofthe intermediate query in a simplified format, the actual questionasked, and the response may be sent back to the user interface. Inaddition, user interactions may be captured. For example, the amount oftime spent by the user in each grid and the feedback button clicked,e.g., Bookmark, Incorrect, Correct or More options represented by 324,may be stored for further training and reinforced learning.

It should be recognized that although several embodiments have beendisclosed, these embodiments are not exclusive and aspects of oneembodiment may be applicable to other embodiments.

Hereinafter, general aspects of implementation of the systems andmethods of the invention will be described.

The system of the invention or portions of the system of the inventionmay be in the form of a “processing machine,” such as a general purposecomputer, for example. As used herein, the term “processing machine” isto be understood to include at least one processor that uses at leastone memory. The at least one memory stores a set of instructions. Theinstructions may be either permanently or temporarily stored in thememory or memories of the processing machine. The processor executes theinstructions that are stored in the memory or memories in order toprocess data. The set of instructions may include various instructionsthat perform a particular task or tasks, such as those tasks describedabove. Such a set of instructions for performing a particular task maybe characterized as a program, software program, or simply software.

In one embodiment, the processing machine may be a specializedprocessor.

As noted above, the processing machine executes the instructions thatare stored in the memory or memories to process data. This processing ofdata may be in response to commands by a user or users of the processingmachine, in response to previous processing, in response to a request byanother processing machine and/or any other input, from automatedscheduling, for example.

As noted above, the processing machine used to implement the inventionmay be a general purpose computer. However, the processing machinedescribed above may also utilize any of a wide variety of othertechnologies including a special purpose computer, a computer systemincluding, for example, a microcomputer, mini-computer or mainframe, aprogrammed microprocessor, a micro-controller, a peripheral integratedcircuit element, a CSIC (Customer Specific Integrated Circuit) or ASIC(Application Specific Integrated Circuit) or other integrated circuit, alogic circuit, a digital signal processor, a programmable logic devicesuch as a FPGA, PLD, PLA or PAL, or any other device or arrangement ofdevices that is capable of implementing the steps of the processes ofthe invention.

The processing machine used to implement the invention may utilize asuitable operating system. Thus, embodiments of the invention mayinclude a processing machine running the iOS operating system, the OS Xoperating system, the Android operating system, the Microsoft Windows™operating systems, the Unix operating system, the Linux operatingsystem, the Xenix operating system, the IBM AIX™ operating system, theHewlett-Packard UX™ operating system, the Novell Netware™ operatingsystem, the Sun Microsystems Solaris™ operating system, the OS/2™operating system, the BeOS™ operating system, the Macintosh operatingsystem, the Apache operating system, an OpenStep™ operating system oranother operating system or platform.

It is appreciated that in order to practice the method of the inventionas described above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. That is, each of the processors and the memoriesused by the processing machine may be located in geographically distinctlocations and connected so as to communicate in any suitable manner.Additionally, it is appreciated that each of the processor and/or thememory may be composed of different physical pieces of equipment.Accordingly, it is not necessary that the processor be one single pieceof equipment in one location and that the memory be another single pieceof equipment in another location. That is, it is contemplated that theprocessor may be two pieces of equipment in two different physicallocations. The two distinct pieces of equipment may be connected in anysuitable manner. Additionally, the memory may include two or moreportions of memory in two or more physical locations.

To explain further, processing, as described above, is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two distinct components as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by twodistinct components. In a similar manner, the memory storage performedby two distinct memory portions as described above may, in accordancewith a further embodiment of the invention, be performed by a singlememory portion. Further, the memory storage performed by one distinctmemory portion as described above may be performed by two memoryportions.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; i.e., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, the Internet,Intranet, Extranet, LAN, an Ethernet, wireless communication via celltower or satellite, or any client server system that providescommunication, for example. Such communications technologies may use anysuitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions may be used in the processingof the invention. The set of instructions may be in the form of aprogram or software. The software may be in the form of system softwareor application software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example. Thesoftware used might also include modular programming in the form ofobject oriented programming. The software tells the processing machinewhat to do with the data being processed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. That is, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, i.e., to a particular type ofcomputer, for example. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, Ada, APL, Basic, C, C++,COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX,Visual Basic, and/or JavaScript, Phyton, for example. Further, it is notnecessary that a single type of instruction or single programminglanguage be utilized in conjunction with the operation of the system andmethod of the invention. Rather, any number of different programminglanguages may be utilized as is necessary and/or desirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module,for example.

As described above, the invention may illustratively be embodied in theform of a processing machine, including a computer or computer system,for example, that includes at least one memory. It is to be appreciatedthat the set of instructions, i.e., the software for example, thatenables the computer operating system to perform the operationsdescribed above may be contained on any of a wide variety of media ormedium, as desired. Further, the data that is processed by the set ofinstructions might also be contained on any of a wide variety of mediaor medium. That is, the particular medium, i.e., the memory in theprocessing machine, utilized to hold the set of instructions and/or thedata used in the invention may take on any of a variety of physicalforms or transmissions, for example. Illustratively, the medium may bein the form of paper, paper transparencies, a compact disk, a DVD, anintegrated circuit, a hard disk, a floppy disk, an optical disk, amagnetic tape, a RAM, a ROM, a PROM, an EPROM, a wire, a cable, a fiber,a communications channel, a satellite transmission, a memory card, a SIMcard, or other remote transmission, as well as any other medium orsource of data that may be read by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements the invention may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In the system and method of the invention, a variety of “userinterfaces” may be utilized to allow a user to interface with theprocessing machine or machines that are used to implement the invention.As used herein, a user interface includes any hardware, software, orcombination of hardware and software used by the processing machine thatallows a user to interact with the processing machine. A user interfacemay be in the form of a dialogue screen for example. A user interfacemay also include any of a mouse, touch screen, keyboard, keypad, voicereader, voice recognizer, dialogue screen, menu box, list, checkbox,toggle switch, a pushbutton or any other device that allows a user toreceive information regarding the operation of the processing machine asit processes a set of instructions and/or provides the processingmachine with information. Accordingly, the user interface is any devicethat provides communication between a user and a processing machine. Theinformation provided by the user to the processing machine through theuser interface may be in the form of a command, a selection of data, orsome other input, for example.

As discussed above, a user interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a user. The user interface is typically usedby the processing machine for interacting with a user either to conveyinformation or receive information from the user. However, it should beappreciated that in accordance with some embodiments of the system andmethod of the invention, it is not necessary that a human user actuallyinteract with a user interface used by the processing machine of theinvention. Rather, it is also contemplated that the user interface ofthe invention might interact, i.e., convey and receive information, withanother processing machine, rather than a human user. Accordingly, theother processing machine might be characterized as a user. Further, itis contemplated that a user interface utilized in the system and methodof the invention may interact partially with another processing machineor processing machines, while also interacting partially with a humanuser.

It will be readily understood by those persons skilled in the art thatthe present invention is susceptible to broad utility and application.Many embodiments and adaptations of the present invention other thanthose herein described, as well as many variations, modifications andequivalent arrangements, will be apparent from or reasonably suggestedby the present invention and foregoing description thereof, withoutdeparting from the substance or scope of the invention.

Accordingly, while the present invention has been described here indetail in relation to its exemplary embodiments, it is to be understoodthat this disclosure is only illustrative and exemplary of the presentinvention and is made to provide an enabling disclosure of theinvention. Accordingly, the foregoing disclosure is not intended to beconstrued or to limit the present invention or otherwise to exclude anyother such embodiments, adaptations, variations, modifications orequivalent arrangements.

What is claimed is:
 1. A system for automated analysis of businessintelligence comprising: a query interface configured to receive anatural language user input; a natural language understanding processorcomprising a parser and interpreter to determine a user intent andgenerate an intermediate query and further based on a context manager; aquery processor configured to translate the intermediate query to adatabase query and execute the database query against a database; and apresentation interface configured to generate a result output thatcomprises results of the database query in natural language.
 2. Thesystem of claim 1, wherein the query interface is provided on anelectronic device.
 3. The system of claim 1, wherein the query isreceived in a dialog with a plurality of interactions with a chatbot. 4.The system of claim 1, wherein the interpreter is in communication withthe context manager and a concept mapper that receives inputs from adomain concept ontology and an acronym dictionary.
 5. The system ofclaim 1, wherein the natural language understanding processor appliesone or more entitlements to generate the intermediate query.
 6. Thesystem of claim 1, wherein the database query is a SQL query.
 7. Thesystem of claim 1, wherein the query processor further parses one ormore graph nodes and relationships to generate the database query. 8.The system of claim 1, wherein the interpreter identifies a plurality ofintents that are ranked.
 9. The system of claim 1, wherein one or moreuser actions associated with the result output are tracked.
 10. Thesystem of claim 1, wherein the one or more user actions comprisesscrolling speed.
 11. A method for automated analysis of businessintelligence, the method comprising the steps of: receiving, via a queryinterface, a natural language user input; determining, via a naturallanguage understanding processor comprising a parser and interpreter, auser intent; generating an intermediate query and further based on acontext data from a context manager; translating, via a query processor,the intermediate query to a database query and executing the databasequery against a database; and generating, via a presentation interface,a result output that comprises results of the database query in naturallanguage.
 12. The method of claim 11, wherein the query interface isprovided on an electronic device.
 13. The method of claim 11, whereinthe query is received in a dialog with a plurality of interactions witha chatbot.
 14. The method of claim 11, wherein the interpreter is incommunication with the context manager and a concept mapper thatreceives inputs from a domain concept ontology and an acronymdictionary.
 15. The method of claim 11, wherein the natural languageunderstanding processor applies one or more entitlements to generate theintermediate query.
 16. The method of claim 11, wherein the databasequery is a SQL query.
 17. The method of claim 11, wherein the queryprocessor further parses one or more graph nodes and relationships togenerate the database query.
 18. The method of claim 11, wherein theinterpreter identifies a plurality of intents that are ranked.
 19. Themethod of claim 11, wherein one or more user actions associated with theresult output are tracked.
 20. The method of claim 11, wherein the oneor more user actions comprises scrolling speed.